41 citations · 71 across the 20 of their papers we have counts for
4 papers · 1 filter
A Unified Framework for Neural Computation and Learning Over Time
Stefano Melacci, Alessandro Betti, Michele Casoni +3
This paper proposes Hamiltonian Learning, a novel unified framework for learning with neural networks "over time", i.e., from a possibly infinite stream of data, in an online manne…
Continual Learning of Conjugated Visual Representations through Higher-order Motion Flows
Simone Marullo, Matteo Tiezzi, Marco Gori +1
Learning with neural networks from a continuous stream of visual information presents several challenges due to the non-i.i.d. nature of the data. However, it also offers novel opp…
State-Space Modeling in Long Sequence Processing: A Survey on Recurrence in the Transformer Era
Matteo Tiezzi, Michele Casoni, Alessandro Betti +2
Effectively learning from sequential data is a longstanding goal of Artificial Intelligence, especially in the case of long sequences. From the dawn of Machine Learning, several re…
On the Resurgence of Recurrent Models for Long Sequences -- Survey and Research Opportunities in the Transformer Era
Matteo Tiezzi, Michele Casoni, Alessandro Betti +3
A longstanding challenge for the Machine Learning community is the one of developing models that are capable of processing and learning from very long sequences of data. The outsta…